Predictive Modeling and Customer Profiling: How auto marketers can expand their customer net

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It doesn’t take an economics guru to tell you that it’s a competitive market out there, and although the economy is heating up and the auto industry is on the rebound, this is no time to be complacent. What separates average automotive marketers from the highest achievers? Here’s the answer: average marketers go after customers already in the market, while the top marketers actually create new customers. Using two techniques that draw on today’s data technology, predictive modeling and customer profiling, it’s easier than ever to do this. Let’s take a closer look.

Predictive Modeling: Identifying Potential Customers

Predictive modeling is a method of creating statistical models to predict future behavior. Through the use of data and predictive modeling, it becomes possible to identify customers who are currently in the market. These include consumers who have made a credit inquiry, consumers experiencing a lifestyle change such as a new job or a move, and those with a short time left on an existing loan or lease.

Even more importantly, though, it becomes possible to identify customers who can be influenced into the market. Here is where the average marketer blossoms into an overachiever. Consumers who may be influenced into the car-buying market include those who are eligible to upgrade without increasing their monthly payments, those who have undergone credit changes that affect their purchasing power and, lastly, those unlikely to repurchase their current brand. This may be due to the brand’s being discontinued or being affected by problems such as recalls or other bad press.

The key to harnessing the power of predictive modeling is to find a data provider who will use both credit and non-credit indicators to help you pinpoint potential auto buyers. A good data provider will not stop there, though, but will also tailor their research to your particular circumstances, helping you learn from your prior marketing campaigns. What offers have been converting? Which demographics are buying—or avoiding—your products? Which channels are most effective? Answering these questions allows you to benefit from the “big three” of marketing advantages: maximizing resources, saving money, and converting customers.

Finally, aside from data, it is important for your data provider to have model-building experience. This allows them to model based on more data variables than you may have access to or, in the case of your having in-house models of your own, to supplement or independently validate them.

It is important to keep in mind the 3 “P”’s of predictive modeling: proactive, predictive, and prescriptive. By sharing information with customers as to what vehicles and offers they may be interested in, it reduces their legwork and allows you to engage with them to help in their decision-making. The farther along they are in their process, of course, the less likely you are to be able to educate them about additional options, as they may already be vested in their decisions or have drawn up their short list. All of this is particularly true for captive lenders: once the customer is building and pricing options on a competitor’s website, they are more vested, and even more so by the time they go to test drive. If you have important information about new models you think that customer should know about, make sure to share it with them as early in their process as possible.

Image from Predictive Analytics book by Eric Siegel

Customer Profiling: Matching Customer to Car

Say you want to buy a gift for a good friend, and you don’t want to spoil the surprise by asking them for a wish list. What do you do? Based on your knowledge of your friend’s likes and dislikes, you make a very good prediction of what your friend would like. Then you buy it and wrap it up.

Customer profiling is just the same. Similar to basing your gift choice on your friend’s likes and dislikes, you can use your knowledge of your customers’ preferences to answer the question: “what segments are the customers most likely to buy?” ” Here’s how it works:

Mine your data. In many cases, the data you need is already right at your disposal: just look at your previous marketing campaigns. Who opened your emails? Who responded to your offers? Who followed through and made the buy?

Narrow down the attributes. From there, you can narrow down the list of potential buyers to a few key attributes.

Check for variance: how accurate were these attributes in determining car preference? The lower the variance, the better the model.

A customer profile does not guarantee that a given consumer will buy a particular car, of course—no model can be 100% predictive. But it does mean that they are more likely to do so. A good customer profile will not only include the highest product match for that particular customer, but also their next-highest and third-highest match as well. As customers may rank highly for several segments, this provides marketers with options. One customer, for example, may be a good match both for an SUV and a minivan, a crossover or an AWD wagon. From a messaging perspective, this allows you to craft both a primary and secondary message or offer: a new Infiniti Q50 lease at $349 a month, or a CPO Infiniti G37 for $28,000, for example.

It’s also important not to go too far. Beware the dangers of over-specification. If you select too many criteria variables it will target your campaigns too narrowly and you may find your return-on-investment decreasing rather than increasing. A good internal team and partner will be able to provide you with insight into such situations, where the padding-out of your criteria with additional elements is not giving you corresponding lift, is not financially optimal and may be causing you to leave opportunities on the table.

The Benefits of Synergy

Advances in data analytics allow automotive marketers to identify customers who are currently in the market, to create potential new customers, and to use profiles to predict their purchasing behavior. Through this synergy between data provider and marketer, resources can be optimized, money saved and, most importantly, the automotive customer can be certain that they are receiving an individualized offer that is appropriate for them. Good marketing and attention to the customer go hand-in-hand.

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About the Author: Kesna Lawrence[LinkedIn] is the SVP for Client Strategy at Datamyx, the top provider of data-driven technology solutions for direct marketing in the financial services, automotive, and insurance industries. Prior to joining the Datamyx team in 2004, Kesna held leadership positions at both ABN AMRO and Ocwen. He holds a B.S. in Finance and Economics from Florida State University.

About the Author

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